I have this code for finding the subset sum of positive values and everywhere I searched I only see positive integers or a program written in java in advanced level. I want to know how to implement that my C program would work with negative numbers. Actually, I want it to find sum that is 0. I had an idea
Take the minimum value in the set, call it k.
Add each element in the set by the absolute value of k.
Add sum by the absolute value of k.
Perform the algorithm.
But I found that this wont work. Take the set (-5, 10) and see if any subset adds up to 5. We would convert (-5, 10) -> (0, 15) and 5->10. -5+10=5, but 0+15 != 10
A lot of ideas I searched on the internet but can't find the answer.
#include <stdio.h>
typedef int bool;
#define true 1
#define false 0
bool isSubsetSum(int set[], int n, int sum) {
// Base Cases
if (sum == 0)
return true;
if (n == 0 && sum != 0)
return false;
if (set[n - 1] > sum)
return isSubsetSum(set, n - 1, sum);
return isSubsetSum(set, n - 1, sum) ||
isSubsetSum(set, n - 1, sum - set[n - 1]);
}
int main() {
int set[] = { -3, 34, -2, 12, 5, 8 };
int sum = 0;
int i;
int n = sizeof(set) / sizeof(set[0]);
if (isSubsetSum(set, n, sum) == true)
printf("Found a subset");
else
printf("No subset");
return 0;
}
I dont really understand your strategy. You should not use the absolute value. The sum of a+b has little to do with the sum of |a|+|b| (well there are some relations, but if you use them somewhere then I missed it ;).
If you have an algorithm that can find you a subset among positive integers that adds up to x, then you can use it also for negative numbers. It wont be as efficient, but with a small trick it can work....
First you add an offset to all numbers to make them all positive. Now you look for subsets that add up to x+y*offset, where y is the size of the subset. Eg. you have
A = -1, -3, -2, 6 12, 48
and you are looking for a subset that adds up to 0, then you first add 3 to all numbers,
b = 2, 0, 1, 9, 15, 51
and then try to find a subset of size 1 that adds up to 3, a subset of size 2 that adds up to 6, ...., a subset of size 4 that adds up to 12, that would be
12 = 2+0+1+9 ie 0 = -1 + -3 + -2 + 6
Doing it that way isnt very efficient, because you have to apply the algorithm N-times (N= size of input). However, if your algorithm for positives lets you fix the size of the subset, this may compensate this loss in efficiency.
I guess you can try a brute force attempt by removing the test for overflow:
#include <stdio.h>
int isSubsetSum(int set[], int n, int sum, int empty_ok) {
// Base Cases
if (sum == 0 && empty_ok)
return 1;
if (n == 0)
return 0;
return isSubsetSum(set, n - 1, sum, empty_ok) ||
isSubsetSum(set, n - 1, sum - set[n - 1], 1);
}
int main(void) {
int set[] = { 3, 34, 2, 12, 5, 8 };
int n = sizeof(set) / sizeof(set[0]);
int sum = 6;
if (isSubsetSum(set, n, sum, 0) == true)
printf("Found a subset");
else
printf("No subset");
return 0;
}
Unfortunately, the time complexity of this solution is O(2n).
Here is a non recursive solution for sets up to 64 elements:
int isSubsetSum(int set[], int n, int sum) {
unsigned long long last;
if (n == 0)
return sum == 0;
last = ((1ULL << (n - 1)) << 1) - 1;
// only find non empty subsets for a 0 sum
for (unsigned long long bits = 1;; bits++) {
int s = 0;
for (int i = 0; i < n; i++) {
s += set[i] * ((bits >> i) & 1);
}
if (s == sum)
return 1;
if (bits == last)
return 0;
}
}
Explanation: type unsigned long long is guaranteed to have at least 64 value bits. bits varies from 1 to last inclusive and takes all possible bit patterns of n bits except all off. For each value of bits, I sum the elements for which the corresponding bit is set, hence all possible non empty subsets are tested.
Code has TBD bug
Yet OP requested it to remain. I fix it later or take down tomorrow.
OP's code has trouble because it is searching for the wrong sum.
By finding the minimum value and offsetting each element of set[], the problem becomes one of only positive numbers - which apparently OP has solved prior.
The trick is that the target sum needs to be offset by n*offset
#include <stdio.h>
#include <stdbool.h>
//typedef int bool;
//#define true 1
//#define false 0
bool isSubsetSum(int set[], int n, int sum, int offset) {
// Base Cases
if ((sum + n*offset) == 0)
return true;
if (n == 0 && (sum + n*offset) != 0)
return false;
if (set[n - 1] > sum + n*offset)
return isSubsetSum(set, n - 1, sum, offset);
return isSubsetSum(set, n - 1, sum, offset) ||
isSubsetSum(set, n - 1, sum - set[n - 1], offset);
}
int main() {
int set[] = { -3, 34, -2, 12, 5, 8 };
int sum = 0;
int i;
int n = sizeof(set) / sizeof(set[0]);
int min = -3; // TBD code to find minimum
for (i = 0; i<6; i++) set[i] -= min;
if (isSubsetSum(set, n, sum, -min) == true)
printf("Found a subset");
else
printf("No subset");
return 0;
}
Found a subset
Related
Here is a code to exponentiate a number to a given power:
#include <stdio.h>
int foo(int m, int k) {
if (k == 0) {
return 1;
} else if (k % 2 != 0) {
return m * foo(m, k - 1);
} else {
int p = foo(m, k / 2);
return p * p;
}
}
int main() {
int m, k;
while (scanf("%d %d", &m, &k) == 2) {
printf("%d\n", foo(m, k));
}
return 0;
}
How do I calculate the time complexity of the function foo?
I have been able to deduce that if k is a power of 2, the time complexity is O(log k).
But I am finding it difficult to calculate for other values of k. Any help would be much appreciated.
How do I calculate the time complexity of the function foo()?
I have been able to deduce that if k is a power of 2, the time complexity is O(logk).
First, I assume that the time needed for each function call is constant (this would for example not be the case if the time needed for a multiplication depends on the numbers being multiplied - which is the case on some computers).
We also assume that k>=1 (otherwise, the function will run endlessly unless there is an overflow).
Let's think the value k as a binary number:
If the rightmost bit is 0 (k%2!=0 is false), the number is shifted right by one bit (foo(m,k/2)) and the function is called recursively.
If the rightmost bit is 1 (k%2!=0 is true), the bit is changed to a 0 (foo(m,k-1)) and the function is called recursively. (We don't look at the case k=1, yet.)
This means that the function is called once for each bit and it is called once for each 1 bit. Or, in other words: It is called once for each 0 bit in the number and twice for each 1 bit.
If N is the number of function calls, n1 is the number of 1 bits and n0 is the number of 0 bits, we get the following formula:
N = n0 + 2*n1 + C
The constant C (C=(-1), if I didn't make a mistake) represents the case k=1 that we ignored up to now.
This means:
N = (n0 + n1) + n1 + C
And - because n0 + n1 = floor(log2(k)) + 1:
floor(log2(k)) + C <= N <= 2*floor(log2(k)) + C
As you can see, the time complexity is always O(log(k))
O(log(k))
Some modification added to output a statistics for spread sheet plot.
#include <stdio.h>
#include <math.h>
#ifndef TEST_NUM
#define TEST_NUM (100)
#endif
static size_t iter_count;
int foo(int m, int k) {
iter_count++;
if (k == 0) {
return 1;
} else if(k == 1) {
return m;
} else if (k % 2 != 0) {
return m * foo(m, k - 1);
} else {
int p = foo(m, k / 2);
return p * p;
}
}
int main() {
for (int i = 1; i < TEST_NUM; ++i) {
iter_count = 0;
int dummy_result = foo(1, i);
printf("%d, %zu, %f\n", i, iter_count, log2(i));
}
return 0;
}
Build it.
gcc t1.c -DTEST_NUM=10000
./a > output.csv
Now open the output file with a spread sheet program and plot the last two output columns.
For k positive, the function foo calls itself recursively p times if k is the p-th power of 2. If k is not a power of 2, the number of recursive calls is strictly inferior to 2 * p where p is the exponent of the largest power of 2 inferior to k.
Here is a demonstration:
let's expand the recursive call in the case k % 2 != 0:
int foo(int m, int k) {
if (k == 1) {
return m;
} else
if (k % 2 != 0) { /* 2 recursive calls */
// return m * foo(m, k - 1);
int p = foo(m, k / 2);
return m * p * p;
} else { /* 1 recursive call */
int p = foo(m, k / 2);
return p * p;
}
}
The total number of calls is floor(log2(k)) + bitcount(k), and bitcount(k) is by construction <= ceil(log2(k)).
There are no loops in the code and the time of each individual call is bounded by a constant, hence the overall time complexity of O(log k).
The number of times the function is called (recursively or not) per power call is proportional to the minimum number of bits in the exponent required to represent it in binary form.
Each time you enter in the function, it solves by reducing the number by one if the exponent is odd, OR reducing it to half if the exponent is even. This means that we will do n squares per significant bit in the number, and m more multiplications by the base for all the bits that are 1 in the exponent (which are, at most, n, so m < n) for a 32bit significant exponent (this is an exponent between 2^31 and 2^32 the routine will do between 32 and 64 products to get the result, and will reenter to itself a maximum of 64 times)
as in both cases the routine is tail-recursive, the code you post can be substituted with an iterative code in which a while loop is used to solve the problem.
int foo(int m, int k)
{
int prod = 1; /* last recursion foo(m, 0); */
int sq = m; /* squares */
while (k) {
if (k & 1) {
prod *= sq; /* foo(m, k); k odd */
}
k >>= 1;
sq *= sq;
}
return prod; /* return final product */
}
That's huge savings!!! (between 32 multiplications and 64 multiplications, to elevate something to 1,000,000,000 power)
I'm facing some difficulties in the last few days while trying to finish the following task, I hope you guys can assist :
I'm given a single number N, and I'm allowed to perform any of the two operations on N in each move :
One - If we take 2 integers where N = x * y , then we can change the value of N to the maximum between x and y.
Two - Decrease the value of N by 1.
I want to find the minimum number of steps to reduce N to zero.
This is what I have so far, I'm not sure what is the best way to implement the function to find the divisor (someFindDevisorFunction), and if this 'f' function would actually produce the required output.
int f(int n)
{
int div,firstWay,secondWay;
if(n == 0)
return 0;
div = SomefindDivisorFunction(n);
firstWay = 1 + f(n-1);
if(div != 1)
{
secondWay = 1 + f(div);
if (firstWay < secondWay)
return firstWay;
return secondWay;
}
return firstWay;
}
For example, if I enter the number 150 , the output would be :
75 - 25 - 5 - 4 - 2 - 1 - 0
I see this a recursive or iterative problem.
OP's approach hints at recursive.
A recursive solution follows:
At each step, code counts the steps of the various alternatives:
steps(n) = min(
steps(factor1_of_n) + 1,
steps(factor2_of_n) + 1,
steps(factor3_of_n) + 1,
...
steps(n-1) + 1)
The coded solution below is inefficient, but it does explore all possibilities and gets to the answer.
int solve_helper(int n, bool print) {
int best_quot = 0;
int best_quot_score = INT_MAX;
int quot;
for (int p = 2; p <= (quot = n / p); p++) {
int rem = n % p;
if (rem == 0 && quot > 1) {
int score = solve_helper(quot, false) + 1;
if (score < best_quot_score) {
best_quot_score = score;
best_quot = quot;
}
}
}
int dec_score = n > 0 ? solve_helper(n - 1, false) + 1 : 0;
if (best_quot_score < dec_score) {
if (print) {
printf("/ %d ", best_quot);
solve_helper(best_quot, true);
}
return best_quot_score;
}
if (print && n > 0) {
printf("- %d ", n - 1);
solve_helper(n - 1, true);
}
return dec_score;
}
int main() {
int n = 75;
printf("%d ", n);
solve(n, true);
printf("\n");
}
Output
75 / 25 / 5 - 4 / 2 - 1 - 0
Iterative
TBD
If you start looking for a divisor with 2, and work your way up, then the last pair of divisors you find will include the largest divisor. Alternatively you can start searching with divisor = N/2 and work down, when the first divisor found will have be largest divisor of N.
int minmoves(int n){
if(n<=3){
return n;
}
int[] dp=new int[n+1];
Arrays.fill(dp,-1);
dp[0]=0;
dp[1]=1;
dp[2]=2;
dp[3]=3;
int sqr;
for(int i=4;i<=n;i++){
sqr=(int)Math.sqrt(i);
int best=Integer.MAX_VALUE;
while(sqr >1){
if(i%sqr==0){
int fact=i/sqr;
best=Math.min(best,1+dp[fact]);
}
sqr--;
}
best=Math.min(best,1+dp[i-1]);
dp[i]=best;
}
return dp[n];
}
Let's say I've been given two integers a, b where a is a positive integer and is smaller than b. I have to find an efficient algorithm that's going to give me the sum of number of base2 digits (number of bits) over the interval [a, b]. For example, in the interval [0, 4] the sum of digits is equal to 9 because 0 = 1 digit, 1 = 1 digit, 2 = 2 digits, 3 = 2 digits and 4 = 3 digits.
My program is capable of calculating this number by using a loop but I'm looking for something more efficient for large numbers. Here are the snippets of my code just to give you an idea:
int numberOfBits(int i) {
if(i == 0) {
return 1;
}
else {
return (int) log2(i) + 1;
}
}
The function above is for calculating the number of digits of one number in the interval.
The code below shows you how I use it in my main function.
for(i = a; i <= b; i++) {
l = l + numberOfBits(i);
}
printf("Digits: %d\n", l);
Ideally I should be able to get the number of digits by using the two values of my interval and using some special algorithm to do that.
Try this code, i think it gives you what you are needing to calculate the binaries:
int bit(int x)
{
if(!x) return 1;
else
{
int i;
for(i = 0; x; i++, x >>= 1);
return i;
}
}
The main thing to understand here is that the number of digits used to represent a number in binary increases by one with each power of two:
+--------------+---------------+
| number range | binary digits |
+==============+===============+
| 0 - 1 | 1 |
+--------------+---------------+
| 2 - 3 | 2 |
+--------------+---------------+
| 4 - 7 | 3 |
+--------------+---------------+
| 8 - 15 | 4 |
+--------------+---------------+
| 16 - 31 | 5 |
+--------------+---------------+
| 32 - 63 | 6 |
+--------------+---------------+
| ... | ... |
A trivial improvement over your brute force algorithm would then be to figure out how many times this number of digits has increased between the two numbers passed in (given by the base two logarithm) and add up the digits by multiplying the count of numbers that can be represented by the given number of digits (given by the power of two) with the number of digits.
A naive implementation of this algorithm is:
int digits_sum_seq(int a, int b)
{
int sum = 0;
int i = 0;
int log2b = b <= 0 ? 1 : floor(log2(b));
int log2a = a <= 0 ? 1 : floor(log2(a)) + 1;
sum += (pow(2, log2a) - a) * (log2a);
for (i = log2b; i > log2a; i--)
sum += pow(2, i - 1) * i;
sum += (b - pow(2, log2b) + 1) * (log2b + 1);
return sum;
}
It can then be improved by the more efficient versions of the log and pow functions seen in the other answers.
First, we can improve the speed of log2, but that only gives us a fixed factor speed-up and doesn't change the scaling.
Faster log2 adapted from: https://graphics.stanford.edu/~seander/bithacks.html#IntegerLogLookup
The lookup table method takes only about 7 operations to find the log
of a 32-bit value. If extended for 64-bit quantities, it would take
roughly 9 operations. Another operation can be trimmed off by using
four tables, with the possible additions incorporated into each. Using
int table elements may be faster, depending on your architecture.
Second, we must re-think the algorithm. If you know that numbers between N and M have the same number of digits, would you add them up one by one or would you rather do (M-N+1)*numDigits?
But if we have a range where multiple numbers appear what do we do? Let's just find the intervals of same digits, and add sums of those intervals. Implemented below. I think that my findEndLimit could be further optimized with a lookup table.
Code
#include <stdio.h>
#include <limits.h>
#include <time.h>
unsigned int fastLog2(unsigned int v)
{
static const char LogTable256[256] =
{
#define LT(n) n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n
-1, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
LT(4), LT(5), LT(5), LT(6), LT(6), LT(6), LT(6),
LT(7), LT(7), LT(7), LT(7), LT(7), LT(7), LT(7), LT(7)
};
register unsigned int t, tt; // temporaries
if (tt = v >> 16)
{
return (t = tt >> 8) ? 24 + LogTable256[t] : 16 + LogTable256[tt];
}
else
{
return (t = v >> 8) ? 8 + LogTable256[t] : LogTable256[v];
}
}
unsigned int numberOfBits(unsigned int i)
{
if (i == 0) {
return 1;
}
else {
return fastLog2(i) + 1;
}
}
unsigned int findEndLimit(unsigned int sx, unsigned int ex)
{
unsigned int sy = numberOfBits(sx);
unsigned int ey = numberOfBits(ex);
unsigned int mx;
unsigned int my;
if (sy == ey) // this also means sx == ex
return ex;
// assumes sy < ey
mx = (ex - sx) / 2 + sx; // will eq. sx for sx + 1 == ex
my = numberOfBits(mx);
while (ex - sx != 1) {
mx = (ex - sx) / 2 + sx; // will eq. sx for sx + 1 == ex
my = numberOfBits(mx);
if (my == ey) {
ex = mx;
ey = numberOfBits(ex);
}
else {
sx = mx;
sy = numberOfBits(sx);
}
}
return sx+1;
}
int main(void)
{
unsigned int a, b, m;
unsigned long l;
clock_t start, end;
l = 0;
a = 0;
b = UINT_MAX;
start = clock();
unsigned int i;
for (i = a; i < b; ++i) {
l += numberOfBits(i);
}
if (i == b) {
l += numberOfBits(i);
}
end = clock();
printf("Naive\n");
printf("Digits: %ld; Time: %fs\n",l, ((double)(end-start))/CLOCKS_PER_SEC);
l=0;
start = clock();
do {
m = findEndLimit(a, b);
l += (b-m + 1) * (unsigned long)numberOfBits(b);
b = m-1;
} while (b > a);
l += (b-a+1) * (unsigned long)numberOfBits(b);
end = clock();
printf("Binary search\n");
printf("Digits: %ld; Time: %fs\n",l, ((double)(end-start))/CLOCKS_PER_SEC);
}
Output
From 0 to UINT_MAX
$ ./main
Naive
Digits: 133143986178; Time: 25.722492s
Binary search
Digits: 133143986178; Time: 0.000025s
My findEndLimit can take long time in some edge cases:
From UINT_MAX/16+1 to UINT_MAX/8
$ ./main
Naive
Digits: 7784628224; Time: 1.651067s
Binary search
Digits: 7784628224; Time: 4.921520s
Conceptually, you would need to split the task to two subproblems -
1) find the sum of digits from 0..M, and from 0..N, then subtract.
2) find the floor(log2(x)), because eg for the number 77 the numbers 64,65,...77 all have 6 digits, the next 32 have 5 digits, the next 16 have 4 digits and so on, which makes a geometric progression.
Thus:
int digits(int a) {
if (a == 0) return 1; // should digits(0) be 0 or 1 ?
int b=(int)floor(log2(a)); // use any all-integer calculation hack
int sum = 1 + (b+1) * (a- (1<<b) +1); // added 1, due to digits(0)==1
while (--b)
sum += (b + 1) << b; // shortcut for (b + 1) * (1 << b);
return sum;
}
int digits_range(int a, int b) {
if (a <= 0 || b <= 0) return -1; // formulas work for strictly positive numbers
return digits(b)-digits(a-1);
}
As efficiency depends on the tools available, one approach would be doing it "analog":
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
unsigned long long pow2sum_min(unsigned long long n, long long unsigned m)
{
if (m >= n)
{
return 1;
}
--n;
return (2ULL << n) + pow2sum_min(n, m);
}
#define LN(x) (log2(x)/log2(M_E))
int main(int argc, char** argv)
{
if (2 >= argc)
{
fprintf(stderr, "%s a b\n", argv[0]);
exit(EXIT_FAILURE);
}
long a = atol(argv[1]), b = atol(argv[2]);
if (0L >= a || 0L >= b || b < a)
{
puts("Na ...!");
exit(EXIT_FAILURE);
}
/* Expand intevall to cover full dimensions: */
unsigned long long a_c = pow(2, floor(log2(a)));
unsigned long long b_c = pow(2, floor(log2(b+1)) + 1);
double log2_a_c = log2(a_c);
double log2_b_c = log2(b_c);
unsigned long p2s = pow2sum_min(log2_b_c, log2_a_c) - 1;
/* Integral log2(x) between a_c and b_c: */
double A = ((b_c * (LN(b_c) - 1))
- (a_c * (LN(a_c) - 1)))/LN(2)
+ (b+1 - a);
/* "Integer"-integral - integral of log2(x)'s inverse function (2**x) between log(a_c) and log(b_c): */
double D = p2s - (b_c - a_c)/LN(2);
/* Corrective from a_c/b_c to a/b : */
double C = (log2_b_c - 1)*(b_c - (b+1)) + log2_a_c*(a - a_c);
printf("Total used digits: %lld\n", (long long) ((A - D - C) +.5));
}
:-)
The main thing here is the number and kind of iterations done.
Number is
log(floor(b_c)) - log(floor(a_c))
times
doing one
n - 1 /* Integer decrement */
2**n + s /* One bit-shift and one integer addition */
for each iteration.
Here's an entirely look-up based approach. You don't even need the log2 :)
Algorithm
First we precompute interval limits where the number of bits would change and create a lookup table. In other words we create an array limits[2^n], where limits[i] gives us the biggest integer that can be represented with (i+1) bits. Our array is then {1, 3, 7, ..., 2^n-1}.
Then, when we want to determine the sum of bits for our range, we must first match our range limits a and b with the smallest index for which a <= limits[i] and b <= limits[j] holds, which will then tell us that we need (i+1) bits to represent a, and (j+1) bits to represent b.
If the indexes are the same, then the result is simply (b-a+1)*(i+1), otherwise we must separately get the number of bits from our value to the edge of same number of bits interval, and add up total number of bits for each interval between as well. In any case, simple arithmetic.
Code
#include <stdio.h>
#include <limits.h>
#include <time.h>
unsigned long bitsnumsum(unsigned int a, unsigned int b)
{
// generate lookup table
// limits[i] is the max. number we can represent with (i+1) bits
static const unsigned int limits[32] =
{
#define LTN(n) n*2u-1, n*4u-1, n*8u-1, n*16u-1, n*32u-1, n*64u-1, n*128u-1, n*256u-1
LTN(1),
LTN(256),
LTN(256*256),
LTN(256*256*256)
};
// make it work for any order of arguments
if (b < a) {
unsigned int c = a;
a = b;
b = c;
}
// find interval of a
unsigned int i = 0;
while (a > limits[i]) {
++i;
}
// find interval of b
unsigned int j = i;
while (b > limits[j]) {
++j;
}
// add it all up
unsigned long sum = 0;
if (i == j) {
// a and b in the same range
// conveniently, this also deals with j == 0
// so no danger to do [j-1] below
return (i+1) * (unsigned long)(b - a + 1);
}
else {
// add sum of digits in range [a, limits[i]]
sum += (i+1) * (unsigned long)(limits[i] - a + 1);
// add sum of digits in range [limits[j], b]
sum += (j+1) * (unsigned long)(b - limits[j-1]);
// add sum of digits in range [limits[i], limits[j]]
for (++i; i<j; ++i) {
sum += (i+1) * (unsigned long)(limits[i] - limits[i-1]);
}
return sum;
}
}
int main(void)
{
clock_t start, end;
unsigned int a=0, b=UINT_MAX;
start = clock();
printf("Sum of binary digits for numbers in range "
"[%u, %u]: %lu\n", a, b, bitsnumsum(a, b));
end = clock();
printf("Time: %fs\n", ((double)(end-start))/CLOCKS_PER_SEC);
}
Output
$ ./lookup
Sum of binary digits for numbers in range [0, 4294967295]: 133143986178
Time: 0.000282s
Algorithm
The main idea is to find the n2 = log2(x) rounded down. That is the number of digits in x. Let pow2 = 1 << n2. n2 * (pow2 - x + 1) is the number of digits in the values [x...pow2]. Now find the sun of digits in the powers of 2 from 1 to n2-1
Code
I am certain various simplifications can be made.
Untested code. Will review later.
// Let us use unsigned for everything.
unsigned ulog2(unsigned value) {
unsigned result = 0;
if (0xFFFF0000u & value) {
value >>= 16; result += 16;
}
if (0xFF00u & value) {
value >>= 8; result += 8;
}
if (0xF0u & value) {
value >>= 4; result += 4;
}
if (0xCu & value) {
value >>= 2; result += 2;
}
if (0x2 & value) {
value >>= 1; result += 1;
}
return result;
}
unsigned bit_count_helper(unsigned x) {
if (x == 0) {
return 1;
}
unsigned n2 = ulog2(x);
unsigned pow2 = 1u << n;
unsigned sum = n2 * (pow2 - x + 1u); // value from pow2 to x
while (n2 > 0) {
// ... + 5*16 + 4*8 + 3*4 + 2*2 + 1*1
pow2 /= 2;
sum += n2 * pow2;
}
return sum;
}
unsigned bit_count(unsigned a, unsigned b) {
assert(a < b);
return bit_count_helper(b - 1) - bit_count_helper(a);
}
For this problem your solution is the simplest, the one called "naive" where you look for every element in the sequence or in your case interval for check something or execute operations.
Naive Algorithm
Assuming that a and b are positive integers with b greater than a let's call the dimension/size of the interval [a,b], n = (b-a).
Having our number of elements n and using some notations of algorithms (like big-O notation link), the worst case cost is O(n*(numberOfBits_cost)).
From this we can see that we can speed up our algorithm by using a faster algorithm for computing numberOfBits() or we need to find a way to not look at every element of the interval that costs us n operations.
Intuition
Now looking at a possible interval [6,14] you can see that for 6 and 7 we need 3 digits, with 4 need for 8,9,10,11,12,13,14. This results in calling numberOfBits() for every number that use the same number of digits to be represented, while the following multiplication operation would be faster:
(number_in_subinterval)*digitsForThisInterval
((14-8)+1)*4 = 28
((7-6)+1)*3 = 6
So we reduced the looping on 9 elements with 9 operations to only 2.
So writing a function that use this intuition will give us a more efficient in time, not necessarily in memory, algorithm. Using your numberOfBits() function I have created this solution:
int intuitionSol(int a, int b){
int digitsForA = numberOfBits(a);
int digitsForB = numberOfBits(b);
if(digitsForA != digitsForB){
//because a or b can be that isn't the first or last element of the
// interval that a specific number of digit can rappresent there is a need
// to execute some correction operation before on a and b
int tmp = pow(2,digitsForA) - a;
int result = tmp*digitsForA; //will containt the final result that will be returned
int i;
for(i = digitsForA + 1; i < digitsForB; i++){
int interval_elements = pow(2,i) - pow(2,i-1);
result = result + ((interval_elements) * i);
//printf("NumOfElem: %i for %i digits; sum:= %i\n", interval_elements, i, result);
}
int tmp1 = ((b + 1) - pow(2,digitsForB-1));
result = result + tmp1*digitsForB;
return result;
}
else {
int elements = (b - a) + 1;
return elements * digitsForA; // or digitsForB
}
}
Let's look at the cost, this algorithm costs is the cost of doing correction operation on a and b plus the most expensive one that of the for-loop. In my solution however I'm not looping over all elements but only on numberOfBits(b)-numberOfBits(a) that in the worst case, when [0,n], become log(n)-1 thats equivalent to O(log n).
To resume we passed from a linear operations cost O(n) to a logartmic one O(log n) in the worst case. Look on this diagram the diferinces between the two.
Note
When I talk about interval or sub-interval I refer to the interval of elements that use the same number of digits to represent the number in binary.
Following there are some output of my tests with the last one that shows the difference:
Considered interval is [0,4]
YourSol: 9 in time: 0.000015s
IntuitionSol: 9 in time: 0.000007s
Considered interval is [0,0]
YourSol: 1 in time: 0.000005s
IntuitionSol: 1 in time: 0.000005s
Considered interval is [4,7]
YourSol: 12 in time: 0.000016s
IntuitionSol: 12 in time: 0.000005s
Considered interval is [2,123456]
YourSol: 1967697 in time: 0.005010s
IntuitionSol: 1967697 in time: 0.000015s
I have removed all the storylines for this question.
Q. You are given N numbers. You have to find 2 equal sum sub-sequences, with maximum sum. You don't necessarily need to use all numbers.
Eg 1:-
5
1 2 3 4 1
Sub-sequence 1 : 2 3 // sum = 5
Sub-sequence 2 : 4 1 // sum = 5
Possible Sub-sequences with equal sum are
{1,2} {3} // sum = 3
{1,3} {4} // sum = 4
{2,3} {4,1} // sum = 5
Out of which 5 is the maximum sum.
Eg 2:-
6
1 2 4 5 9 1
Sub-sequence 1 : 2 4 5 // sum = 11
Sub-sequence 2 : 1 9 1 // sum = 11
The maximum sum you can get is 11
Constraints:
5 <= N <= 50
1<= number <=1000
sum of all numbers is <= 1000
Important: Only <iostream> can be used. No STLs.
N numbers are unsorted.
If array is not possible to split, print 0.
Number of function stacks is limited. ie your recursive/memoization solution won't work.
Approach 1:
I tried a recursive approach something like the below:
#include <iostream>
using namespace std;
bool visited[51][1001][1001];
int arr[51];
int max_height=0;
int max_height_idx=0;
int N;
void recurse( int idx, int sum_left, int sum_right){
if(sum_left == sum_right){
if(sum_left > max_height){
max_height = sum_left;
max_height_idx = idx;
}
}
if(idx>N-1)return ;
if(visited[idx][sum_left][sum_right]) return ;
recurse( idx+1, sum_left+arr[idx], sum_right);
recurse( idx+1, sum_left , sum_right+arr[idx]);
recurse( idx+1, sum_left , sum_right);
visited[idx][sum_left][sum_right]=true;
/*
We could reduce the function calls, by check the visited condition before calling the function.
This could reduce stack allocations for function calls. For simplicity I have not checking those conditions before function calls.
Anyways, this recursive solution would get time out. No matter how you optimize it.
Btw, there are T testcases. For simplicity, removed that constraint.
*/
}
int main(){
ios_base::sync_with_stdio(false);
cin.tie(nullptr);
cin>>N;
for(int i=0; i<N; i++)
cin>>arr[i];
recurse(0,0,0);
cout<< max_height <<"\n";
}
NOTE: Passes test-cases. But time out.
Approach 2:
I also tried, taking advantage of constraints.
Every number has 3 possible choice:
1. Be in sub-sequence 1
2. Be in sub-sequence 2
3. Be in neither of these sub-sequences
So
1. Be in sub-sequence 1 -> sum + 1*number
2. Be in sub-sequence 2 -> sum + -1*number
3. None -> sum
Maximum sum is in range -1000 to 1000.
So dp[51][2002] could be used to save the maximum positive sum achieved so far (ie till idx).
CODE:
#include <iostream>
using namespace std;
int arr[51];
int N;
int dp[51][2002];
int max3(int a, int b, int c){
return max(a,max(b,c));
}
int max4(int a, int b, int c, int d){
return max(max(a,b),max(c,d));
}
int recurse( int idx, int sum){
if(sum==0){
// should i perform anything here?
}
if(idx>N-1){
return 0;
}
if( dp[idx][sum+1000] ){
return dp[idx][sum+1000];
}
return dp[idx][sum+1000] = max3 (
arr[idx] + recurse( idx+1, sum + arr[idx]),
0 + recurse( idx+1, sum - arr[idx]),
0 + recurse( idx+1, sum )
) ;
/*
This gives me a wrong output.
4
1 3 5 4
*/
}
int main(){
ios_base::sync_with_stdio(false);
cin.tie(nullptr);
cin>>N;
for(int i=0; i<N; i++)
cin>>arr[i];
cout<< recurse(0,0) <<"\n";
}
The above code gives me wrong answer. Kindly help me with solving/correcting this memoization.
Also open to iterative approach for the same.
Idea of your second approach is correct, it's basically a reduction to the knapsack problem. However, it looks like your code lacks clear contract: what the recurse function is supposed to do.
Here is my suggestion: int recurse(int idx, int sum) distributes elements on positions idx..n-1 into three multisets A, B, C such that sum+sum(A)-sum(B)=0 and returns maximal possible sum(A), -inf otherwise (here -inf is some hardcoded constant which serves as a "marker" of no answer; there are some restrictions on it, I suggest -inf == -1000).
Now you're to write a recursive backtracking using that contract and then add memoization. Voila—you've got a dynamic programming solution.
In recursive backtracking we have two distinct situations:
There are no more elements to distribute, no choices to make: idx == n. In that case, we should check that our condition holds (sum + sum(A) - sum(B) == 0, i.e. sum == 0) and return the answer. If sum == 0, then the answer is 0. However, if sum != 0, then there is no answer and we should return something which will never be chosen as the answer, unless there are no answer for the whole problem. As we modify returning value of recurse and do not want extra ifs, it cannot be simply zero or even -1; it should be a number which, when modified, still remains "the worst answer ever". The biggest modification we can make is to add all numbers to the resulting value, hence we should choose something less or equal to negative maximal sum of numbers (i.e. -1000), as existing answers are always strictly positive, and that fictive answer will always be non-positive.
There is at least one remaining element which should be distributed to either A, B or C. Make the choice and choose the best answer among three options. Answers are calculated recursively.
Here is my implementation:
const int MAXN = 50;
const int MAXSUM = 1000;
bool visited[MAXN + 1][2 * MAXSUM + 1]; // should be filled with false
int dp[MAXN + 1][2 * MAXSUM + 1]; // initial values do not matter
int recurse(int idx, int sum){
// Memoization.
if (visited[idx][sum + MAXSUM]) {
return dp[idx][sum + MAXSUM];
}
// Mark the current state as visited in the beginning,
// it's ok to do before actually computing it as we're
// not expect to visit it while computing.
visited[idx][sum + MAXSUM] = true;
int &answer = dp[idx][sum + MAXSUM];
// Backtracking search follows.
answer = -MAXSUM; // "Answer does not exist" marker.
if (idx == N) {
// No more choices to make.
if (sum == 0) {
answer = 0; // Answer exists.
} else {
// Do nothing, there is no answer.
}
} else {
// Option 1. Current elemnt goes to A.
answer = max(answer, arr[idx] + recurse(idx + 1, sum + arr[idx]));
// Option 2. Current element goes to B.
answer = max(answer, recurse(idx + 1, sum - arr[idx]));
// Option 3. Current element goes to C.
answer = max(answer, recurse(idx + 1, sum));
}
return answer;
}
Here is a simple dynamic programming based solution for anyone interested, based on the idea suggested by Codeforces user lemelisk here. Complete post here. I haven't tested this code completely though.
#include <iostream>
using namespace std;
#define MAXN 20 // maximum length of array
#define MAXSUM 500 // maximum sum of all elements in array
#define DIFFSIZE (2*MAXSUM + 9) // possible size of differences array (-maxsum, maxsum) + some extra
int dp[MAXN][DIFFSIZE] = { 0 };
int visited[DIFFSIZE] = { 0 }; // visited[diff] == 1 if the difference 'diff' can be reached
int offset = MAXSUM + 1; // offset so that indices in dp table don't become negative
// 'diff' replaced by 'offset + diff' below everywhere
int max(int a, int b) {
return (a > b) ? a : b;
}
int max_3(int a, int b, int c) {
return max(a, max(b, c));
}
int main() {
int a[] = { 1, 2, 3, 4, 6, 7, 5};
int n = sizeof(a) / sizeof(a[0]);
int *arr = new int[n + 1];
int sum = 0;
for (int i = 1; i <= n; i++) {
arr[i] = a[i - 1]; // 'arr' same as 'a' but with 1-indexing for simplicity
sum += arr[i];
} // 'sum' holds sum of all elements of array
for (int i = 0; i < MAXN; i++) {
for (int j = 0; j < DIFFSIZE; j++)
dp[i][j] = INT_MIN;
}
/*
dp[i][j] signifies the maximum value X that can be reached till index 'i' in array such that diff between the two sets is 'j'
In other words, the highest sum subsets reached till index 'i' have the sums {X , X + diff}
See http://codeforces.com/blog/entry/54259 for details
*/
// 1 ... i : (X, X + diff) can be reached by 1 ... i-1 : (X - a[i], X + diff)
dp[0][offset] = 0; // subset sum is 0 for null set, difference = 0 between subsets
visited[offset] = 1; // initially zero diff reached
for (int i = 1; i <= n; i++) {
for (int diff = (-1)*sum; diff <= sum; diff++) {
if (visited[offset + diff + arr[i]] || visited[offset + diff - arr[i]] || visited[offset + diff]) {
// if difference 'diff' is reachable, then only update, else no need
dp[i][offset + diff] = max_3
(
dp[i - 1][offset + diff],
dp[i - 1][offset + diff + arr[i]] + arr[i],
dp[i - 1][offset + diff - arr[i]]
);
visited[offset + diff] = 1;
}
}
/*
dp[i][diff] = max {
dp[i - 1][diff] : not taking a[i] in either subset
dp[i - 1][diff + arr[i]] + arr[i] : putting arr[i] in first set, thus reducing difference to 'diff', increasing X to X + arr[i]
dp[i - 1][diff - arr[i]] : putting arr[i] in second set
initialization: dp[0][0] = 0
*/
// O(N*SUM) algorithm
}
cout << dp[n][offset] << "\n";
return 0;
}
Output:
14
State is not updated in Approach 1. Change the last line of recurse
visited[idx][sum_left][sum_right];
to
visited[idx][sum_left][sum_right] = 1;
Also memset the visited array to false before calling recurse from main.
I am trying to solve an online judge problem: http://opc.iarcs.org.in/index.php/problems/LEAFEAT
The problem in short:
If we are given an integer L and a set of N integers s1,s2,s3..sN, we have to find how many numbers there are from 0 to L-1 which are not divisible by any of the 'si's.
For example, if we are given, L = 20 and S = {3,2,5} then there are 6 numbers from 0 to 19 which are not divisible by 3,2 or 5.
L <= 1000000000 and N <= 20.
I used the Inclusion-Exclusion principle to solve this problem:
/*Let 'T' be the number of integers that are divisible by any of the 'si's in the
given range*/
for i in range 1 to N
for all subsets A of length i
if i is odd then:
T += 1 + (L-1)/lcm(all the elements of A)
else
T -= 1 + (L-1)/lcm(all the elements of A)
return T
Here is my code to solve this problem
#include <stdio.h>
int N;
long long int L;
int C[30];
typedef struct{int i, key;}subset_e;
subset_e A[30];
int k;
int gcd(a,b){
int t;
while(b != 0){
t = a%b;
a = b;
b = t;
}
return a;
}
long long int lcm(int a, int b){
return (a*b)/gcd(a,b);
}
long long int getlcm(int n){
if(n == 1){
return A[0].key;
}
int i;
long long int rlcm = lcm(A[0].key,A[1].key);
for(i = 2;i < n; i++){
rlcm = lcm(rlcm,A[i].key);
}
return rlcm;
}
int next_subset(int n){
if(k == n-1 && A[k].i == N-1){
if(k == 0){
return 0;
}
k--;
}
while(k < n-1 && A[k].i == A[k+1].i-1){
if(k <= 0){
return 0;
}
k--;
}
A[k].key = C[A[k].i+1];
A[k].i++;
return 1;
}
int main(){
int i,j,add;
long long int sum = 0,g,temp;
scanf("%lld%d",&L,&N);
for(i = 0;i < N; i++){
scanf("%d",&C[i]);
}
for(i = 1; i <= N; i++){
add = i%2;
for(j = 0;j < i; j++){
A[j].key = C[j];
A[j].i = j;
}
temp = getlcm(i);
g = 1 + (L-1)/temp;
if(add){
sum += g;
} else {
sum -= g;
}
k = i-1;
while(next_subset(i)){
temp = getlcm(i);
g = 1 + (L-1)/temp;
if(add){
sum += g;
} else {
sum -= g;
}
}
}
printf("%lld",L-sum);
return 0;
}
The next_subset(n) generates the next subset of size n in the array A, if there is no subset it returns 0 otherwise it returns 1. It is based on the algorithm described by the accepted answer in this stackoverflow question.
The lcm(a,b) function returns the lcm of a and b.
The get_lcm(n) function returns the lcm of all the elements in A.
It uses the property : LCM(a,b,c) = LCM(LCM(a,b),c)
When I submit the problem on the judge it gives my a 'Time Limit Exceeded'. If we solve this using brute force we get only 50% of the marks.
As there can be upto 2^20 subsets my algorithm might be slow, hence I need a better algorithm to solve this problem.
EDIT:
After editing my code and changing the function to the Euclidean algorithm, I am getting a wrong answer, but my code runs within the time limit. It gives me a correct answer to the example test but not to any other test cases; here is a link to ideone where I ran my code, the first output is correct but the second is not.
Is my approach to this problem correct? If it is then I have made a mistake in my code, and I'll find it; otherwise can anyone please explain what is wrong?
You could also try changing your lcm function to use the Euclidean algorithm.
int gcd(int a, int b) {
int t;
while (b != 0) {
t = b;
b = a % t;
a = t;
}
return a;
}
int lcm(int a, int b) {
return (a * b) / gcd(a, b);
}
At least with Python, the speed differences between the two are pretty large:
>>> %timeit lcm1(103, 2013)
100000 loops, best of 3: 9.21 us per loop
>>> %timeit lcm2(103, 2013)
1000000 loops, best of 3: 1.02 us per loop
Typically, the lowest common multiple of a subset of k of the s_i will exceed L for k much smaller than 20. So you need to stop early.
Probably, just inserting
if (temp >= L) {
break;
}
after
while(next_subset(i)){
temp = getlcm(i);
will be sufficient.
Also, shortcut if there are any 1s among the s_i, all numbers are divisible by 1.
I think the following will be faster:
unsigned gcd(unsigned a, unsigned b) {
unsigned r;
while(b) {
r = a%b;
a = b;
b = r;
}
return a;
}
unsigned recur(unsigned *arr, unsigned len, unsigned idx, unsigned cumul, unsigned bound) {
if (idx >= len || bound == 0) {
return bound;
}
unsigned i, g, s = arr[idx], result;
g = s/gcd(cumul,s);
result = bound/g;
for(i = idx+1; i < len; ++i) {
result -= recur(arr, len, i, cumul*g, bound/g);
}
return result;
}
unsigned inex(unsigned *arr, unsigned len, unsigned bound) {
unsigned i, result = bound, t;
for(i = 0; i < len; ++i) {
result -= recur(arr, len, i, 1, bound);
}
return result;
}
call it with
unsigned S[N] = {...};
inex(S, N, L-1);
You need not add the 1 for the 0 anywhere, since 0 is divisible by all numbers, compute the count of numbers 1 <= k < L which are not divisible by any s_i.
Create an array of flags with L entries. Then mark each touched leaf:
for(each size in list of sizes) {
length = 0;
while(length < L) {
array[length] = TOUCHED;
length += size;
}
}
Then find the untouched leaves:
for(length = 0; length < L; length++) {
if(array[length] != TOUCHED) { /* Untouched leaf! */ }
}
Note that there is no multiplication and no division involved; but you will need up to about 1 GiB of RAM. If RAM is a problem the you can use an array of bits (max. 120 MiB).
This is only a beginning though, as there are repeating patterns that can be copied instead of generated. The first pattern is from 0 to S1*S2, the next is from 0 to S1*S2*S3, the next is from 0 to S1*S2*S3*S4, etc.
Basically, you can set all values touched by S1 and then S2 from 0 to S1*S2; then copy the pattern from 0 to S1*S2 until you get to S1*S2*S3 and set all the S3's between S3 and S1*S2*S3; then copy that pattern until you get to S1*S2*S3*S4 and set all the S4's between S4 and S1*S2*S3*S4 and so on.
Next; if S1*S2*...Sn is smaller than L, you know the pattern will repeat and can generate the results for lengths from S1*S2*...Sn to L from the pattern. In this case the size of the array only needs to be S1*S2*...Sn and doesn't need to be L.
Finally, if S1*S2*...Sn is larger than L; then you could generate the pattern for S1*S2*...(Sn-1) and use that pattern to create the results from S1*S2*...(Sn-1) to S1*S2*...Sn. In this case if S1*S2*...(Sn-1) is smaller than L then the array doesn't need to be as large as L.
I'm afraid your problem understanding is maybe not correct.
You have L. You have a set S of K elements. You must count the sum of quotient of L / Si. For L = 20, K = 1, S = { 5 }, the answer is simply 16 (20 - 20 / 5). But K > 1, so you must consider the common multiples also.
Why loop through a list of subsets? It doesn't involve subset calculation, only division and multiple.
You have K distinct integers. Each number could be a prime number. You must consider common multiples. That's all.
EDIT
L = 20 and S = {3,2,5}
Leaves could be eaten by 3 = 6
Leaves could be eaten by 2 = 10
Leaves could be eaten by 5 = 4
Common multiples of S, less than L, not in S = 6, 10, 15
Actually eaten leaves = 20/3 + 20/2 + 20/5 - 20/6 - 20/10 - 20/15 = 6
You can keep track of the distance until then next touched leaf for each size. The distance to the next touched leaf will be whichever distance happens to be smallest, and you'd subtract this distance from all the others (and wrap whenever the distance is zero).
For example:
int sizes[4] = {2, 5, 7, 9};
int distances[4];
int currentLength = 0;
for(size = 0 to 3) {
distances[size] = sizes[size];
}
while(currentLength < L) {
smallest = INT_MAX;
for(size = 0 to 3) {
if(distances[size] < smallest) smallest = distances[size];
}
for(size = 0 to 3) {
distances[size] -= smallest;
if(distances[size] == 0) distances[size] = sizes[size];
}
while( (smallest > 1) && (currentLength < L) ) {
currentLength++;
printf("%d\n", currentLength;
smallest--;
}
}
#A.06: u r the one with username linkinmew on opc, rite?
Anyways, the answer just requires u to make all possible subsets, and then apply inclusion exclusion principle. This will fall well within the time bounds for the data given. For making all possible subsets, u can easily define a recursive function.
i don't know about programming but in math there is a single theorem which works on a set that has GCD 1
L=20, S=(3,2,5)
(1-1/p)(1-1/q)(1-1/r).....and so on
(1-1/3)(1-1/2)(1-1/5)=(2/3)(1/2)(4/5)=4/15
4/15 means there are 4 numbers in each set of 15 number which are not divisible by any number rest of it can be count manually eg.
16, 17, 18, 19, 20 (only 17 and 19 means there are only 2 numbers thatr can't be divided by any S)
4+2=6
6/20 means there are only 6 numbers in first 20 numbers that can't be divided by any s